Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ to n = 30 data points and obtain the following result: y ̂=3.4-4.6x_1+2.7x_2+0.93x_3 The estimated standard errors of β ̂_2 and β...
Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ to n = 30 data points and obtain the following result: y ̂=3.4-4.6x_1+2.7x_2+0.93x_3 The estimated standard errors of β ̂_2 and β ̂_3 are 1.86 and .29, respectively. Test the null hypothesis H0: β2 = 0 against the alternative hypothesis Ha: β2 ≠0. Use α = .05. Test the null hypothesis H0: β3 = 0 against the alternative hypothesis Ha: β3 ≠0. Use α = .05. The null hypothesis H0: β2 = 0 is not rejected. In contrast, the null hypothesis H0: β3 = 0 is rejected. Explain how this can happen even though β ̂_2 > β ̂_3.
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Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ to n = 30 data points and obtain the following result: y ̂=3.4-4.6x_1+2.7x_2+0.93x_3 The estimated standard errors of β ̂_2 and β...
Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ...
Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ to n = 30 data points and obtain the following result: y ̂=3.4-4.6x_1+2.7x_2+0.93x_3 The estimated standard errors of β ̂_2 and β ̂_3 are 1.86 and .29, respectively. Test the null hypothesis H0: β2 = 0 against the alternative hypothesis Ha: β2 ≠0. Use α = .05. Test the null hypothesis H0: β3 = 0 against the alternative hypothesis Ha: β3 ≠0. Use α...
31. Suppose you fit a multiple linear regression model y = β0 + β1x1 + β2x2...
31. Suppose you fit a multiple linear regression model y = β0 + β1x1 + β2x2 + β3x3 + β4x4 + ε to n = 30 data points and obtain SSE = 282 and R^2 = 0.8266 a.) Find an estimate of s^2 for the multiple regression model (a) s^2 ≈ 30.9856 (b) s^2 ≈ 28.6021 (c) s^2 ≈ 1.3111 (d) s^2 ≈ 29.7938 (d) b.) Based on the data information given in a.), you use F-test to test H0...
When estimating y = β0 + β1x1 + β2x2 + β3x3 + ε, you wish to...
When estimating y = β0 + β1x1 + β2x2 + β3x3 + ε, you wish to test H0: β1 = β2 = 0 versus HA: At least one βi ≠ 0. The value of the test statistic is F(2,20) = 2.50 and its associated p-value is 0.1073. At the 5% significance level, the conclusion is to ________. Multiple Choice a. reject the null hypothesis; we can conclude that x1 and x2 are jointly significant b. not reject the null hypothesis;...
12.3 Suppose you fit the multiple regression model y = Bo + B1x1 + Bzxz +...
12.3 Suppose you fit the multiple regression model y = Bo + B1x1 + Bzxz + Bzxz + e to n = 30 data points and obtain the following result: ŷ = 3.4 - 4.6x + 2.7x2 + .93xz The estimated standard errors of B, and Bs are 1.86 and .29, respectively. a. Test the null hypothesis Ho: B2 = 0 against the alternative hypothesis He: B2 + 0. Use a = .05. b. Test the null hypothesis Ho: Bz...
1.The following tables give the results for the full model, as well as a reduced model, containing only experience Test Ho: ß,-Bs-0 vs HA: β2 and/or β3 # 0 Complete Model: Y-βο + β1X1 + β2X2 + β3Xs +...
1.The following tables give the results for the full model, as well as a reduced model, containing only experience Test Ho: ß,-Bs-0 vs HA: β2 and/or β3 # 0 Complete Model: Y-βο + β1X1 + β2X2 + β3Xs + ε ANOVA MS P-value df 76.9 Regression Residual Total 2470.4 823.5 224.7 2695.1 .0000 10.7 21 24 Reduced Model: Y = β0 + β X + ε MS df 1 23 24 value 2394.9 2394.9 183.5 0.0000 300.2 13.1 2695.1 Regression...
Suppose you fit the first rder mu ple egression model y = po + β1x1 + a. Test Ho βι-o against h, ...
Suppose you fit the first rder mu ple egression model y = po + β1x1 + a. Test Ho βι-o against h, β1 , 0 Use α-D 05 b. Find a 99% confidence interval for P2 interpret the interval 2x2 + ε to n= 25 data points and obtain the prediction equation y = 37.1 + 1.19x1 + 1 3 2 Th° estimated standard deviations ofthe sampling distributions of βι and P2 are 0.23 and 0.18, espec ve y ....
Suppose that you fitted the model E(y) = β0 + β1x + β2x2 to n =...
Suppose that you fitted the model E(y) = β0 + β1x + β2x2 to n = 20 data points and obtained the following MINITAB printout. Regression Analysis: y versus x, x-sq Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value Regression 2 41225.4 20612.7 987.09 0.000 Error 17 355.0 20.9 Total 19 41580.4 Model Summary S R-Sq R-Sq(adj) 4.56972 99.15% 99.05% Coefficients Term Coef SE Coef T-Value P-Value Constant 12.53 3.40 3.69 0.002 x 9.74 1.49 6.54 0.000...
Statistical software was used to fit the model E(y)Pox1 2x2 to n 20 data points. Complete parts a...
Section 12.3 Multiple Linear Regression:
Number ONE:
Statistical software was used to fit the model E(y)Pox1 2x2 to n 20 data points. Complete parts a through h EEB Click the icon to see the software output. Data Table The regression equation is Y-1738.93 - 384.54x1 517.39x2 Predictor Constant X1 X2 Coef 1738.93 - 384.54 -517.39 SE Coef 369.06 101.65 - 3.78 0.002 353.04 - 1.47 0.162 4.71 0.000 s-172.003 R-sq-55.0% R-sq(adj):49.0% Analysis of Variance MS Source Regression Residual Error 17...
Question 2 1 pts suppose you estimate the following model: Y-α + β1 X1 + β2X2...
Question 2 1 pts suppose you estimate the following model: Y-α + β1 X1 + β2X2 + γΖ + u You wish to test the null hypothesis: Ho; A-:-As against a two-sided alternative. You do so, and get the following estimates: βι 5.23, B2--4.56, 8e (A) 2.09, 8e (%) 1.47, 8e (A-A) 2.24, 8e (A +%)-0.94 What is the value of the relevant test statistic for this hypothesis test? 4.37 0.71 0.30 10.41
Suppose a statistician built a multiple regression model for predicting the total number of runs scored...
Suppose a statistician built a multiple regression model for predicting the total number of runs scored by a baseball team during a season. Using data for n=200 samples, the results below were obtained. Complete parts a throughd. Ind. Var.Ind. Var. β estimate Standard Error Ind. Var. β estimate Standard Error InterceptIntercept 3.88 17.03 Doubles (x3) 0.74 0.04 Walks (x1) 0.37 0.05 Triples (x4) 1.17 0.23 Singles (x2) 0.51 0.05 Home Runs x5 1.44 0.04 a. Write the least squares prediction...
12.3 Suppose you fit the multiple regression model y = Bo + B1x1 + Bzxz + Bzxz + e to n = 30 data points and obtain the following result: ŷ = 3.4 - 4.6x + 2.7x2 + .93xz The estimated standard errors of B, and Bs are 1.86 and .29, respectively. a. Test the null hypothesis Ho: B2 = 0 against the alternative hypothesis He: B2 + 0. Use a = .05. b. Test the null hypothesis Ho: Bz...
1.The following tables give the results for the full model, as well as a reduced model, containing only experience Test Ho: ß,-Bs-0 vs HA: β2 and/or β3 # 0 Complete Model: Y-βο + β1X1 + β2X2 + β3Xs + ε ANOVA MS P-value df 76.9 Regression Residual Total 2470.4 823.5 224.7 2695.1 .0000 10.7 21 24 Reduced Model: Y = β0 + β X + ε MS df 1 23 24 value 2394.9 2394.9 183.5 0.0000 300.2 13.1 2695.1 Regression...
Suppose you fit the first rder mu ple egression model y = po + β1x1 + a. Test Ho βι-o against h, β1 , 0 Use α-D 05 b. Find a 99% confidence interval for P2 interpret the interval 2x2 + ε to n= 25 data points and obtain the prediction equation y = 37.1 + 1.19x1 + 1 3 2 Th° estimated standard deviations ofthe sampling distributions of βι and P2 are 0.23 and 0.18, espec ve y ....
Section 12.3 Multiple Linear Regression:
Number ONE:
Statistical software was used to fit the model E(y)Pox1 2x2 to n 20 data points. Complete parts a through h EEB Click the icon to see the software output. Data Table The regression equation is Y-1738.93 - 384.54x1 517.39x2 Predictor Constant X1 X2 Coef 1738.93 - 384.54 -517.39 SE Coef 369.06 101.65 - 3.78 0.002 353.04 - 1.47 0.162 4.71 0.000 s-172.003 R-sq-55.0% R-sq(adj):49.0% Analysis of Variance MS Source Regression Residual Error 17...
Question 2 1 pts suppose you estimate the following model: Y-α + β1 X1 + β2X2 + γΖ + u You wish to test the null hypothesis: Ho; A-:-As against a two-sided alternative. You do so, and get the following estimates: βι 5.23, B2--4.56, 8e (A) 2.09, 8e (%) 1.47, 8e (A-A) 2.24, 8e (A +%)-0.94 What is the value of the relevant test statistic for this hypothesis test? 4.37 0.71 0.30 10.41
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